agentbench-qwen3-4b

AgentBench Qwen3-4B (ALFWorld + DBBench) v4

This repository provides a LoRA-merged model fine-tuned from Qwen/Qwen3-4B-Instruct-2507.

Training Objective

This model is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn environment observation, action selection, tool use, and recovery from errors.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: LoRA + merge (bf16 full precision)
• Max sequence length: 4096
• Epochs: 1
• Learning rate: 1e-05
• LoRA: r=64, alpha=128
• Batch size: 2 x 4 = 8
• DBBench upsample: x2

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "tkmrkt/agentbench-qwen3-4b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

Sources & Terms (IMPORTANT)

Training data: u-10bei/sft_alfworld_trajectory_dataset_v5, u-10bei/dbbench_sft_dataset_react_v2, u-10bei/dbbench_sft_dataset_react_v3, u-10bei/dbbench_sft_dataset_react_v4